Gait is a basic characteristic of human motion. Different gaits are usually associated with different body functions. Gait recognition has wide applications in clinical medicine, rehabilitation training, posture recognition, and other fields. At present, many wearable systems based on MEMS pressure and inertial sensors have been developed and used for gait recognition. However, there has been a lack of a comprehensive summary and comparison of these systems from the perspectives of their hardware compositions, working principles, algorithm models, and applications. This review aims to promote the development of wearable gait recognition devices. First, sensor technologies for gait measurement are summarized from the perspectives of their working principles and characteristics. Then, these technologies are compared in terms of the performance of algorithms for data preprocessing, cycle and phase segmentation, and gait recognition. Next, the applications of MEMS sensor-based gait recognition systems in various fields are summarized. Finally, some limitations of existing wearable gait recognition systems and their future directions are discussed.

Cell microinjection is a technique of precise delivery of substances into cells and is widely used for studying cell transfection, signaling pathways, and organelle functions. Microinjection of the embryos of zebrafish, the third most important animal model, has become a very useful technique in bioscience. However, factors such as the small cell size, high cell deformation tendency, and transparent zebrafish embryo membrane make the microinjection process difficult. Furthermore, this process has strict, specific requirements, such as chorion softening, avoiding contacting the first polar body, and high-precision detection. Therefore, highly accurate control and detection platforms are critical for achieving the automated microinjection of zebrafish embryos. This article reviews the latest technologies and methods used in the automated microinjection of zebrafish embryos and provides a detailed description of the current developments and applications of robotic microinjection systems. The review covers key areas related to automated embryo injection, including cell searching and location, cell position and posture adjustment, microscopic visual servoing control, sensors, actuators, puncturing mechanisms, and microinjection.

Small defects on the rails develop fast under the continuous load of passing trains, and this may lead to train derailment and other disasters. In recent years, many types of wireless sensor systems have been developed for rail defect detection. However, there has been a lack of comprehensive reviews on the working principles, functions, and trade-offs of these wireless sensor systems. Therefore, we provide in this paper a systematic review of recent studies on wireless sensor-based rail defect detection systems from three different perspectives: sensing principles, wireless networks, and power supply. We analyzed and compared six sensing methods to discuss their detection accuracy, detectable types of defects, and their detection efficiency. For wireless networks, we analyzed and compared their application scenarios, the advantages and disadvantages of different network topologies, and the capabilities of different transmission media. From the perspective of power supply, we analyzed and compared different power supply modules in terms of installation and energy harvesting methods, and the amount of energy they can supply. Finally, we offered three suggestions that may inspire the future development of wireless sensor-based rail defect detection systems.

Background: Traditional Chinese medicine (TCM) tongue diagnosis, through the comprehensive observation of tongue’s diverse characteristics, allows an understanding of the state of the body’s viscera as well as Qi and blood levels. Automatic tongue image recognition methods could support TCM practitioners by providing auxiliary diagnostic suggestions. However, most learning-based methods often address a narrow scope of the tongue’s attributes, failing to fully exploit the information contained within the tongue images. Objective: To classify multifaceted tongue characteristics, and fully utilize the latent correlation information between tongue segmentation and classification tasks, we proposed a multi-task joint learning network for simultaneous tongue body segmentation and multi-label Classification, named SSC-Net. Methods: Firstly, the shared feature encoder extracts features for both segmentation and classification tasks, where the segmentation result is utilized to mask redundant features that may impede classification accuracy. Subsequently, the ROI extraction module locates and extracts the tongue body region, and the feature fusion module combines tongue body features from bottom to top. Finally, a fine-grained classification module is employed for multi-label classification on multiple tongue characteristics. Results: To evaluate the performance of the SSC-Net, we collected a tongue image dataset, BUCM, and conducted extensive experiments on it. The experimental results show that the proposed method when segmenting and classifying simultaneously, achieved 0.9943 DSC for the segmentation task, 92.02 mAP, and 0.851 overall F1-score for the classification task. Conclusion: The proposed method can effectively classify multiple tongue characteristics with the support of the multi-task learning strategy and the integration of a fine-grained classification module. Code is available here.

An IoT wristband for basketball shooting analysis, which can provide quantitative shooting guidance for basketball players in a convenient, low-cost manner, was developed. A micro inertial measurement unit (IMU) sensor-based wristband was used to collect the shooting motion data of 20 basketball players with different levels of skills: 15 amateur players and 5 elite players. The wristband was 247 mm ${}\times {}20$ mm ${}\times {}11$ mm in size, weighed 12.3 g, and consisted of a power system, a microcontroller, and an IMU sensor (MPU-9250), which can acquire the triaxial data of acceleration, angular velocity, and magnetic field data, and then transmit them to a computing platform via Bluetooth. After correlation analysis, the triaxial data of accelerations and angular velocities were used to identify these 18 shooting movements. Experimental results showed that the four categories of shooting movements (i.e., set shots, layups, jump shots, and tip-ins) could be recognized with an accuracy of 98.0%. The overall recognition accuracy of 18 kinds of shooting movements reached 98.5%. The AI wristband could also be used to distinguish the identity of the participants with a recognition rate of 97.4%. This new technology can be applied to quantitative basketball shot-making guidance and has potential applications for analyzing the motions of other sports such as table tennis, racquetball, volleyball, and soccer.

Micromanipulation is an interdisciplinary technology that integrates advanced knowledge of microscale/nanoscale science, mechanical engineering, electronic engineering, and control engineering. Over the past two decades, it has been widely applied in the fields of MEMS (microelectromechanical systems), bioengineering, and microdevice integration and manufacturing. Microvision servoing is the basic tool for enabling the automatic and precise micromanipulation of microscale/nanoscale entities. However, there are still many problems surrounding microvision servoing in theory and the application of this technology’s micromanipulation processes. This paper summarizes the research, development status, and practical applications of critical components of microvision servoing for micromanipulation, including geometric calibration, autofocus techniques, depth information, and visual servoing control. Suggestions for guiding future innovation and development in this field are also provided in this review.